后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

经鼻导管高流量吸氧与经鼻气道正压通气在重症毛细支气管炎呼吸支持中的应用价值比较

目的：比较经鼻导管高流量吸氧（HFNC）与经鼻气道正压通气（nCPAP）在重症毛细支气管炎呼吸支持中的应用价值，为临床治疗方案的选择提供参考。方法：选取2016年12月至2018年12月我院儿科收治的重症毛细支气管炎患儿90例，采用随机数字表法分为观察组和对照组各45例。两组患儿入院后均给予常规综合治疗以保证呼吸道通畅，在此基础上观察组采用HFNC治疗，对照组采用nCPAP治疗，比较两组患儿治疗前和治疗24 h后呼吸频率、经皮血氧饱和度（TcSO2）、呼吸窘迫评分体系（CSS）评分、动脉血氧分压（PaO2）等呼吸相关指标及治疗前后临床症状体征改善情况。结果：两组患儿治疗24 h后呼吸频率、CSS评分均降低，且观察组降低程度更大，TcSO2、PaO2于治疗24 h后升高，观察组升高幅度较对照组明显；治疗后两组患儿咳嗽及肺部湿啰音、肺部炎症情况均改善，观察组症状体征消失时间早于对照组，差异均有统计学意义（P<0.05）。结论：重症毛细支气管炎患儿采用HFNC治疗可明显改善通气功能和临床症状，治疗效果优于nCPAP治疗，可扩大样本量进一步观察。     

国内外护理学本科生毕业考核现状与分析

教育测量与评价对实现教育目标具有重要作用。科学的教育评价既促进学生学习,又为教育决策提供依据,以实现持续质量改进。"健康中国2030"的整体目标、医学科学的发展及人民的健康需求,对新时代护理人才的培养提出了更高、更具体的要求。高等护理院校应以国家和社会对护理人才需求、院校培养目标为导向,以测量理论为基础,制定完善的学业评定标准,科学地开展毕业考核,保证人才"出口"质量。毕业考核作为终结性评价,测试内容在评价质量中起核心作用,应符合国家护理学专业培养标准,并与院校培养目标、课程目标契合。院校应关注护理本科生毕业考核质量,在保障测试信度的同时,做到实施公平、评价适当、标准一致,以确保测试的效度。教育管理部门及各院校应优化评价体系,加强对护理本科生毕业管理,在充分开展系统调查与分析的基础上,基于胜任力导向和结果导向,建立综合性评价体系,在教学过程中进行全方位反馈评价管理。文章结合国内外护理学专业本科生毕业考核的现状,从考核目的、内容、方式、评价主体、质量控制等方面进行综述与分析,为我国加强护理学本科教育,完善护理学专业本科生毕业评价体系建设提供借鉴。     

早发型子痫前期应用低分子肝素期待治疗的临床效果分析

目的分析早发型子痫前期应用低分子肝素期待治疗的临床效果。方法回顾分析2017-01—2018-12间在郑州大学第一附属医院产科终止妊娠的95例早发型子痫前期患者的临床资料。按终止妊娠前是否应用低分子肝素分为2组。对照组(47例)给予降压、解痉等治疗;观察组(48例)在对照组基础上加用低分子肝素。结果2组分娩孕周、妊娠延长时间、妊娠并发症发生率、新生儿出生体质量、新生儿窒息及胎儿宫内窘迫发生率、新生儿Apgar评分等,差异均无统计学意义(P>0.05)。结论对早发型子痫前期患者在常规治疗基础上短期应用低分子肝素,不能延长妊娠时间,不改善母婴结局。     

Prevalence and pathophysiology of early dumping in patients after primary Roux-en-Y gastric bypass during a mixed-meal tolerance test

Background

Early dumping is a poorly defined and incompletely understood complication after Roux-en-Y gastric (RYGB).

Application of metagenomic next-generation sequencing for suspected infected pancreatic necrosis

BackgroundMetagenomic next-generation sequencing (mNGS) is increasingly used for the clinical diagnosis of infectious diseases, but there is a paucity of data regarding the application of mNGS in the early diagnosis of infected pancreatic necrosis (IPN).ObjectiveTo investigate the clinical application value of mNGS in the pathogenic diagnosis of IPN.MethodsForty-two patients with suspected IPN were prospectively and consecutively enrolled from August 2019 to August 2021. Blood samples were collected for mNGS and microbial culture simultaneously during fever (T ≥ 38.5 °C). For patients who had indications of surgical interventions, peri-pancreatic specimens were collected for mNGS and microbial culture simultaneously during the first surgical intervention to confirm IPN. The clinical performance of mNGS and microbial culture were compared.ResultsA total of 21 patients (50.0%) were confirmed to have IPN during hospitalization. The sensitivity of blood mNGS was significantly higher than blood culture (95.2% vs. 23.8%, P < 0.001) in diagnosing IPN. The negative predictive value of blood mNGS was 90.0%. The turnaround time of mNGS was significantly shorter than that of microbial culture [(37.70 ± 1.44) vs. (115.23 ± 8.79) h, P < 0.01] and the average costs of mNGS accounted for 1.7% of the average total cost of hospitalization. The survival analysis demonstrates that the positive blood mNGS result was not associated with increased mortality (P = 0.119).ConclusionsWith more valuable diagnostic performance and shorter turnaround time, clinical mNGS represents a potential step forward in the early diagnosis of IPN.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).